Exposure and confidence

October 21, 2016, Hopkins Marine Station, Stanford University

Outline

My origins in programming, data science, and open science
Improving reproducibility, collaboration and communication in environmental science with open science tools
- Lowndes et al., in prep
- reproducibility is fundamental to science, but rarely tested
- these tools have fundamentally changed how we do science
Resources and recommendations
- exposure and confidence: learning the tools available and using them

Data science and open science

Data Science:

"an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge" (Grolemund & Wickham 2016)

Data science and open science

Data Science:

"an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge" (Grolemund & Wickham 2016)

Data science and open science

Data Science:

"an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge" (Grolemund & Wickham 2016)

Open Science:

"the concept of transparency at all stages of the research process, coupled with free and open access to data, code, and papers" (Hampton et al. 2014)

Open science tools

links: R, RStudio, git, GitHub

Open science workflow

Open science workflow

this talk: jules32.github.io/opensci-talk

written in RStudio's RMarkdown
versioned with Git
shared with GitHub

My programming origins story

Photo credit: Greg Auger

Some thesis questions

what are Humboldt squid habitat preferences?
what season are they most abundant?
how fast and far can they migrate?
how do they interact with other species?
how do I import my data when it's too big for Excel?
how do I subset years or other attributes?
how do I visualize this?
how on earth do I even think about this?

Conflated questions

Science:

what are their habitat preferences?
what season are they most abundant?
how fast and far can they migrate?
how do they interact with other species?

Data science:

how do I import my data when it's too big for Excel?
how do I subset years or other attributes?
how do I visualize this?
how on earth do I even think about this?

I learned to program like many do

in a panic
for a single purpose (get this thesis done!)
in isolation*

I learned to program like many do

in a panic
for a single purpose (get this thesis done!)
in near-isolation*

* except for wonderful programming mentors:

Steve Haddock, Dave Foley, Ashley Booth

NCEAS, UC Santa Barbara

TODO: image

Ocean Health Index

method to categorize benefits that oceans provide to people

scores are modeled using existing data; data intensive

Ocean Health Index

method to categorize benefits that oceans provide to people

scores are modeled using existing data; data intensive

method can be tailored to different geographies

can help inform policy decisions, especially when repeated

OHI Global Assessments

2012: OHI method and first global assessment (Halpern et al. 2012)

OHI Global Assessments

2013: second annual global assessment

repeat methods
update data
compare between years

OHI Global Assessments

2013: second annual global assessment

repeat methods
update data
compare between years

We expected to easily reproduce our previous work. We had planned ahead:

coded models
130 pages of published supplemental material
internal documents and notes

We thought we were doing reproducible science

We struggled to reproduce our work using standard approaches to reproducibility and collaboration

We thought we were doing reproducible science

We struggled to reproduce our work using standard approaches to reproducibility and collaboration

…mainly due to our approaches to data preparation…i.e. data science

We thought we were doing reproducible science

We struggled to reproduce our work using standard approaches to reproducibility and collaboration

…mainly due to our approaches to data preparation…i.e. data science

added challenge of managing multiple years of information and scores
we needed a nimble approach to sharing data, methods, and results within and outside our team

We identified three main challenges to overcome

reproducibility, including transparency and repeatability, esp. data prep
collaboration, including teamwork and internal collaboration
communication with scientific and public communities

We identified three main challenges to overcome

reproducibility, including transparency and repeatability, esp. data prep
collaboration, including teamwork and internal collaboration
communication with scientific and public communities

Lowndes et al. Improving reproducibility, collaboration, and communication in environmental science using open science tools, in prep

exposure and confidence
evolution rather than revolution

Addressing challenges using open science tools

Reproducibility - data preparation

"Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in the mundane labor of collecting and preparing data, before it can be explored for useful information." - NYTimes (2014)